community[patch]: docstrings (#16810)

- added missed docstrings
- formated docstrings to the consistent form

libs/community/langchain_community/adapters/openai.py

@@ -40,25 +40,33 @@ async def aenumerate(
 
 
 class IndexableBaseModel(BaseModel):
-    """Allows a BaseModel to return its fields by string variable indexing"""
+    """Allows a BaseModel to return its fields by string variable indexing."""
 
     def __getitem__(self, item: str) -> Any:
         return getattr(self, item)
 
 
 class Choice(IndexableBaseModel):
+    """Choice."""
+
     message: dict
 
 
 class ChatCompletions(IndexableBaseModel):
+    """Chat completions."""
+
     choices: List[Choice]
 
 
 class ChoiceChunk(IndexableBaseModel):
+    """Choice chunk."""
+
     delta: dict
 
 
 class ChatCompletionChunk(IndexableBaseModel):
+    """Chat completion chunk."""
+
     choices: List[ChoiceChunk]
 
 
@@ -301,7 +309,7 @@ def convert_messages_for_finetuning(
 
 
 class Completions:
-    """Completion."""
+    """Completions."""
 
     @overload
     @staticmethod
@@ -399,6 +407,8 @@ class Completions:
 
 
 class Chat:
+    """Chat."""
+
     def __init__(self) -> None:
         self.completions = Completions()
 

libs/community/langchain_community/agent_toolkits/openapi/planner.py

@@ -191,7 +191,7 @@ class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool):
 
 
 class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool):
-    """A tool that sends a DELETE request and parses the response."""
+    """Tool that sends a DELETE request and parses the response."""
 
     name: str = "requests_delete"
     """The name of the tool."""

libs/community/langchain_community/callbacks/mlflow_callback.py

@@ -39,6 +39,7 @@ def import_mlflow() -> Any:
 
 
 def mlflow_callback_metrics() -> List[str]:
+    """Get the metrics to log to MLFlow."""
     return [
         "step",
         "starts",
@@ -59,6 +60,7 @@ def mlflow_callback_metrics() -> List[str]:
 
 
 def get_text_complexity_metrics() -> List[str]:
+    """Get the text complexity metrics from textstat."""
     return [
         "flesch_reading_ease",
         "flesch_kincaid_grade",

libs/community/langchain_community/callbacks/streamlit/streamlit_callback_handler.py

@@ -225,7 +225,7 @@ class LLMThought:
 
 
 class StreamlitCallbackHandler(BaseCallbackHandler):
-    """A callback handler that writes to a Streamlit app."""
+    """Callback handler that writes to a Streamlit app."""
 
     def __init__(
         self,

libs/community/langchain_community/chat_message_histories/sql.py

@@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
 
 
 class BaseMessageConverter(ABC):
-    """The class responsible for converting BaseMessage to your SQLAlchemy model."""
+    """Class that converts BaseMessage to the SQLAlchemy model."""
 
     @abstractmethod
     def from_sql_model(self, sql_message: Any) -> BaseMessage:

libs/community/langchain_community/chat_models/azureml_endpoint.py

@@ -20,6 +20,8 @@ from langchain_community.llms.azureml_endpoint import (
 
 
 class LlamaContentFormatter(ContentFormatterBase):
+    """Content formatter for `LLaMA`."""
+
     def __init__(self) -> None:
         raise TypeError(
             "`LlamaContentFormatter` is deprecated for chat models. Use "
@@ -34,7 +36,7 @@ class LlamaChatContentFormatter(ContentFormatterBase):
 
     @staticmethod
     def _convert_message_to_dict(message: BaseMessage) -> Dict:
-        """Converts message to a dict according to role"""
+        """Converts a message to a dict according to a role"""
         content = cast(str, message.content)
         if isinstance(message, HumanMessage):
             return {

libs/community/langchain_community/chat_models/deepinfra.py

@@ -58,6 +58,8 @@ logger = logging.getLogger(__name__)
 
 
 class ChatDeepInfraException(Exception):
+    """Exception raised when the DeepInfra API returns an error."""
+
     pass
 
 
@@ -67,7 +69,7 @@ def _create_retry_decorator(
         Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
     ] = None,
 ) -> Callable[[Any], Any]:
-    """Returns a tenacity retry decorator, preconfigured to handle PaLM exceptions"""
+    """Returns a tenacity retry decorator, preconfigured to handle PaLM exceptions."""
     return create_base_retry_decorator(
         error_types=[requests.exceptions.ConnectTimeout, ChatDeepInfraException],
         max_retries=llm.max_retries,

libs/community/langchain_community/chat_models/gpt_router.py

@@ -53,6 +53,8 @@ class GPTRouterException(Exception):
 
 
 class GPTRouterModel(BaseModel):
+    """GPTRouter model."""
+
     name: str
     provider_name: str
 

libs/community/langchain_community/chat_models/volcengine_maas.py

@@ -39,8 +39,8 @@ def convert_dict_to_message(_dict: Mapping[str, Any]) -> AIMessage:
 
 
 class VolcEngineMaasChat(BaseChatModel, VolcEngineMaasBase):
+    """Volc Engine Maas hosts a plethora of models.
 
-    """volc engine maas hosts a plethora of models.
     You can utilize these models through this class.
 
     To use, you should have the ``volcengine`` python package installed.

libs/community/langchain_community/chat_models/zhipuai.py

@@ -20,11 +20,15 @@ logger = logging.getLogger(__name__)
 
 
 class ref(BaseModel):
+    """Reference used in CharacterGLM."""
+
     enable: bool = Field(True)
     search_query: str = Field("")
 
 
 class meta(BaseModel):
+    """Metadata used in CharacterGLM."""
+
     user_info: str = Field("")
     bot_info: str = Field("")
     bot_name: str = Field("")

libs/community/langchain_community/document_loaders/chm.py

@@ -9,7 +9,7 @@ if TYPE_CHECKING:
 class UnstructuredCHMLoader(UnstructuredFileLoader):
     """Load `CHM` files using `Unstructured`.
 
-    CHM mean Microsoft Compiled HTML Help.
+    CHM means Microsoft Compiled HTML Help.
 
     Examples
     --------
@@ -35,6 +35,8 @@ class UnstructuredCHMLoader(UnstructuredFileLoader):
 
 
 class CHMParser(object):
+    """Microsoft Compiled HTML Help (CHM) Parser."""
+
     path: str
     file: "chm.CHMFile"
 

libs/community/langchain_community/document_loaders/parsers/vsdx.py

@@ -11,6 +11,8 @@ from langchain_community.document_loaders.blob_loaders import Blob
 
 
 class VsdxParser(BaseBlobParser, ABC):
+    """Parser for vsdx files."""
+
     def parse(self, blob: Blob) -> Iterator[Document]:  # type: ignore[override]
         """Parse a vsdx file."""
         return self.lazy_parse(blob)

libs/community/langchain_community/document_loaders/youtube.py

@@ -141,6 +141,8 @@ def _parse_video_id(url: str) -> Optional[str]:
 
 
 class TranscriptFormat(Enum):
+    """Transcript format."""
+
     TEXT = "text"
     LINES = "lines"
 

libs/community/langchain_community/embeddings/mlflow.py

@@ -13,7 +13,7 @@ def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
 
 
 class MlflowEmbeddings(Embeddings, BaseModel):
-    """Wrapper around embeddings LLMs in MLflow.
+    """Embedding LLMs in MLflow.
 
     To use, you should have the `mlflow[genai]` python package installed.
     For more information, see https://mlflow.org/docs/latest/llms/deployments/server.html.
@@ -85,5 +85,7 @@ class MlflowEmbeddings(Embeddings, BaseModel):
 
 
 class MlflowCohereEmbeddings(MlflowEmbeddings):
+    """Cohere embedding LLMs in MLflow."""
+
     query_params: Dict[str, str] = {"input_type": "search_query"}
     documents_params: Dict[str, str] = {"input_type": "search_document"}

libs/community/langchain_community/embeddings/oci_generative_ai.py

@@ -8,6 +8,8 @@ CUSTOM_ENDPOINT_PREFIX = "ocid1.generativeaiendpoint"
 
 
 class OCIAuthType(Enum):
+    """OCI authentication types as enumerator."""
+
     API_KEY = 1
     SECURITY_TOKEN = 2
     INSTANCE_PRINCIPAL = 3

libs/community/langchain_community/graphs/neo4j_graph.py

@@ -32,7 +32,8 @@ RETURN {start: label, type: property, end: toString(other_node)} AS output
 
 
 def value_sanitize(d: Dict[str, Any]) -> Dict[str, Any]:
-    """
+    """Sanitize the input dictionary.
+
     Sanitizes the input dictionary by removing embedding-like values,
     lists with more than 128 elements, that are mostly irrelevant for
     generating answers in a LLM context. These properties, if left in
@@ -63,7 +64,8 @@ def value_sanitize(d: Dict[str, Any]) -> Dict[str, Any]:
 
 
 class Neo4jGraph(GraphStore):
-    """Provides a connection to a Neo4j database for various graph operations.
+    """Neo4j database wrapper for various graph operations.
+
     Parameters:
     url (Optional[str]): The URL of the Neo4j database server.
     username (Optional[str]): The username for database authentication.

libs/community/langchain_community/graphs/neptune_graph.py

@@ -2,7 +2,7 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 
 
 class NeptuneQueryException(Exception):
-    """A class to handle queries that fail to execute"""
+    """Exception for the Neptune queries."""
 
     def __init__(self, exception: Union[str, Dict]):
         if isinstance(exception, dict):

libs/community/langchain_community/llms/oci_generative_ai.py

@@ -15,6 +15,8 @@ VALID_PROVIDERS = ("cohere", "meta")
 
 
 class OCIAuthType(Enum):
+    """OCI authentication types as enumerator."""
+
     API_KEY = 1
     SECURITY_TOKEN = 2
     INSTANCE_PRINCIPAL = 3

libs/community/langchain_community/llms/tongyi.py

@@ -91,7 +91,9 @@ def stream_generate_with_retry(llm: Tongyi, **kwargs: Any) -> Any:
 
 
 async def astream_generate_with_retry(llm: Tongyi, **kwargs: Any) -> Any:
-    """Because the dashscope SDK doesn't provide an async API,
+    """Async version of `stream_generate_with_retry`.
+
+    Because the dashscope SDK doesn't provide an async API,
     we wrap `stream_generate_with_retry` with an async generator."""
 
     class _AioTongyiGenerator:

libs/community/langchain_community/retrievers/arcee.py

@@ -10,7 +10,7 @@ from langchain_community.utilities.arcee import ArceeWrapper, DALMFilter
 
 
 class ArceeRetriever(BaseRetriever):
-    """Document retriever for Arcee's Domain Adapted Language Models (DALMs).
+    """Retriever for Arcee's Domain Adapted Language Models (DALMs).
 
     To use, set the ``ARCEE_API_KEY`` environment variable with your Arcee API key,
     or pass ``arcee_api_key`` as a named parameter.

libs/community/langchain_community/storage/astradb.py

@@ -25,6 +25,8 @@ V = TypeVar("V")
 
 
 class AstraDBBaseStore(Generic[V], BaseStore[str, V], ABC):
+    """Base class for the DataStax AstraDB data store."""
+
     def __init__(
         self,
         collection_name: str,
@@ -79,6 +81,7 @@ class AstraDBBaseStore(Generic[V], BaseStore[str, V], ABC):
 
 class AstraDBStore(AstraDBBaseStore[Any]):
     """BaseStore implementation using DataStax AstraDB as the underlying store.
+
     The value type can be any type serializable by json.dumps.
     Can be used to store embeddings with the CacheBackedEmbeddings.
     Documents in the AstraDB collection will have the format
@@ -97,6 +100,7 @@ class AstraDBStore(AstraDBBaseStore[Any]):
 
 class AstraDBByteStore(AstraDBBaseStore[bytes], ByteStore):
     """ByteStore implementation using DataStax AstraDB as the underlying store.
+
     The bytes values are converted to base64 encoded strings
     Documents in the AstraDB collection will have the format
     {

libs/community/langchain_community/tools/polygon/last_quote.py

@@ -8,6 +8,8 @@ from langchain_community.utilities.polygon import PolygonAPIWrapper
 
 
 class Inputs(BaseModel):
+    """Inputs for Polygon's Last Quote API"""
+
     query: str
 
 

libs/community/langchain_community/utilities/vertexai.py

@@ -57,7 +57,7 @@ def init_vertexai(
     location: Optional[str] = None,
     credentials: Optional["Credentials"] = None,
 ) -> None:
-    """Init vertexai.
+    """Init Vertex AI.
 
     Args:
         project: The default GCP project to use when making Vertex API calls.

libs/community/langchain_community/vectorstores/kdbai.py

@@ -16,6 +16,8 @@ logger = logging.getLogger(__name__)
 class KDBAI(VectorStore):
     """`KDB.AI` vector store.
 
+     See [https://kdb.ai](https://kdb.ai)
+
     To use, you should have the `kdbai_client` python package installed.
 
     Args:
@@ -25,7 +27,7 @@ class KDBAI(VectorStore):
         distance_strategy: One option from DistanceStrategy.EUCLIDEAN_DISTANCE,
             DistanceStrategy.DOT_PRODUCT or DistanceStrategy.COSINE.
 
-    See the example https://github.com/KxSystems/langchain/blob/KDB.AI/docs/docs/integrations/vectorstores/kdbai.ipynb.
+    See the example [notebook](https://github.com/KxSystems/langchain/blob/KDB.AI/docs/docs/integrations/vectorstores/kdbai.ipynb).
     """
 
     def __init__(

libs/community/langchain_community/vectorstores/lantern.py

@@ -47,12 +47,14 @@ def _results_to_docs(docs_and_scores: Any) -> List[Document]:
 
 
 class BaseEmbeddingStore:
-    """Embedding store."""
+    """Base class for the Lantern embedding store."""
 
 
 def get_embedding_store(
     distance_strategy: DistanceStrategy, collection_name: str
 ) -> Any:
+    """Get the embedding store class."""
+
     embedding_type = None
 
     if distance_strategy == DistanceStrategy.HAMMING: